1. Field of the Invention
The present invention relates to a method of fingerprint recognition, more particularly, relates to partial fingerprint recognition using minutiae and pores information.
2. Description of the Related Art
Forensic experts have used fingerprints to identify persons for more than one hundred years. They compare latent fingerprints collected from crime scenes (usually partial fingerprints) with the prints taken from suspects (could be either full or partial fingerprints). When examining fingerprints, an expert usually makes use of the so-called minutia features such as endings and bifurcations of ridges on them. More often than not, some other features, for example sweat pores on fingers, are also employed by forensic experts to infer the fingerprints' identities, especially when only small fingerprints are available.
Besides forensic applications, fingerprints are nowadays widely used in various civil applications such as access and attendance control thanks to the fast development of automatic fingerprint recognition techniques. A notable trend in this field is that a small fingerprint sensing area is becoming more and more preferred. This is not only because it can reduce the manufacturing cost, but also because it can make the system more portable.
With such sensors, only small fingerprint regions can be captured on the obtained fragmentary fingerprint images. Therefore, it is highly necessary to exploit novel features and to develop new methods suitable for partial fingerprint recognition.
As one of the oldest and most widely used biometric traits, lots of techniques have been proposed in the prior art using fingerprints to identify individuals and lots of fingerprint related patents have been issued around the world. Among these techniques and patents, most are for recognizing full fingerprints or fingerprint images covering large fingerprint areas. Other techniques for fingerprint recognition include matching input fragmentary (or partial) fingerprints with full (large area) template fingerprints. When small area sensors are used, full template fingerprints are constructed from a number of partial fingerprint images captured during enrollment. When an input fragmentary fingerprint image is given, it will be compared with the constructed full template fingerprints to do authentication. However, for one thing, it is expensive to collect sufficient fingerprint fragments to construct a reliable fingerprint template. For another, errors (e.g. spurious features) could be introduced in the construction process. In addition, all these methods are mainly based on minutiae, which could be very few on small partial fingerprint images. Consequently, it is very possible for them to result in incorrect matches because of insufficient minutiae, or sometimes they are even not applicable for some partial fingerprints when there are no minutiae on them.
Fingerprint alignment is a vital step in fingerprint recognition. The problem of fingerprint alignment or registration originates from that one fingerprint image is often captured at a pose different from previous ones, for example some translation, rotation, or non-rigid deformation could occur between different prints. The goal of fingerprint alignment is thus to retrieve the transformation parameters between fingerprint images and remove the transformation between them. Although non-rigid deformation or distortion could happen in capturing fingerprint images, it is very costly to model and remedy such distortion in fingerprint registration and it can be compensated to some extent in subsequent fingerprint matching. For this sake, the majority of existing fingerprint alignment methods in the literature merely considers translation and rotation though some deformable models have been proposed. And it has been shown that considering only translation and rotation already works well in most cases.
Given two fingerprint images and a setting of transformation parameters, the most intuitive way to evaluate the given transformation is to apply the transformation to one fingerprint image (or selective regions on it) and then calculate the correlation between the transformed fingerprint and the other one. The higher the correlation is, the better the transformation. Using this method, one has to search in the parameter space and find the setting which gives the highest correlation. Such method has also been used by “Study of the Distinctiveness of Level 2 and Level 3 Features in Fragmentary Fingerprint Comparison”, Kryszczuk et al., to align high resolution fingerprint fragments with full fingerprint templates. This correlation based approach is often time consuming and its accuracy is highly dependent on the way the parameter space is quantized.
The most widely used alignment methods are based on minutiae. Their basic idea is to search in the transformation parameter space and find the optimal transformation which allows the maximum number of minutiae to be matched. One representative kind of approaches uses the idea of Hough transform. These methods discretize the transformation parameters (including translation, rotation and scaling) into finite sets of values and then accumulate evidences for possible transformations by checking each minutia on the fingerprints. The transformation obtaining the most evidence is the best alignment transformation for the fingerprints. One disadvantage of these methods is that the discretization leads to inaccuracy in the transformation estimation. Other methods avoid such discretization, but first determine the correspondences between minutiae and then estimate the transformation based on the corresponding minutiae. The minutia correspondences can be determined by simply taking a brute force approach to examine all possible correspondences between minutiae. From each two corresponding minutiae, a transformation is estimated based on their locations and directions and it is then applied to all other minutiae on one fingerprint. The transformed minutiae are compared with the minutiae on the other fingerprint. If both the locations and directions of two minutiae do not differ much (i.e. within a tolerance), they are matched. The transformation which leads to the largest number of matched minutiae is taken as the best alignment transformation for the two fingerprints. To make the obtained minutia correspondences more accurate, many other supplementary features have been exploited in matching minutiae. These features include ridge information associated with minutiae, orientation fields surrounding minutiae, the geometrical relationship between minutiae and their neighboring minutiae, etc. These minutia-based methods perform very well on images covering large fingerprint regions where sufficient minutiae are presented.
Other efforts have been made to employ minutiae to align fingerprint fragments to full fingerprint templates, as disclosed in “A Minutia-based Partial Fingerprint Recognition System, by Jea et al. Unfortunately, when fragmentary fingerprints with small fingerprint regions are given, it would be very possibly that no sufficient minutiae are available. As a result, all these methods will fail. Therefore, some other features rather than minutiae are needed. Besides minutiae, core points and points of maximum curvature of the concave ridges have also been used to align fingerprints. However, these points are vaguely defined and difficult to be accurately extracted. Moreover, they could be very likely not captured on small fingerprint fragments and some fingerprints (e.g. plain arches) do not have such points at all. Hence, they are not proper for fragmentary fingerprint alignment too.
Some other non-minutiae based alignment methods have been recently proposed by Yager and Amin “Fingerprint Alignment Using a Two Stage Optimization”. The method utilized the orientation fields on fingerprints and searched in transformation parameter space for the optimum one which can make the orientation fields match best. To guide the search, they defined a cost function which measures the difference between elements in the common area of two orientation fields. They presented three different approaches to search in the parameter space while minimizing the cost function. One of these three methods, namely steepest descent, starts from an initial estimate and gradually approaches to the local minimum on the cost surface. This method was proved to be most effectively in the authors' experiments. An important advantage of such orientation field based alignment methods is that the orientation fields can be reliably extracted even from fingerprint images of poor quality and they are robust to nonlinear deformation. One potential problem, however, is that these methods prefer solutions which correspond to smaller overlap between orientation fields and thus might converge to wrong solutions. Therefore, they need to set a minimum amount of overlap between fingerprints.
Liu et al., “Fingerprint Registration by Maximization of Mutual Information,” proposed another orientation field based fingerprint alignment method by maximizing the mutual information between orientation fields. It is worth attention that these orientation field based methods are more appropriate for coarse alignment and some further fine tuning of the transformation parameters is necessary. Besides, they require quantizing the transformation parameter space and are thus limited in the estimation accuracy.
Some patents have been issued on fingerprint image alignment methods, such as patents to U.S. Pat. No. 5,717,777 (Wong et al.), U.S. Pat. No. 6,041,133 (Califano et al.), and U.S. Pat. No. 6,314,197 (Jain et al.). The method of Wong et al. requires to define a core region, which could be however unfeasible on partial fingerprints. All the methods in the other two patents are based on minutia features. As discussed before, they are unsuitable for aligning two fingerprint fragments too.
To summarize, alignment of full fingerprints is a well-studied problem. Notwithstanding, existing alignment methods are still limited in their accuracy of alignment transformation estimation due to quantization of transformation parameters or not proper for fragmentary fingerprints because of insufficient features available for them on the small fingerprint fragments. Recent development in automatic fingerprint recognition system (AFRS), however, shows that people have increasing interests on small fingerprint sensors. In addition, the continuing need of law enforcement also requires solutions for fragmentary fingerprint recognition. Some other authors have studied the problem of matching fragmentary fingerprints to full fingerprint templates. All of them still employ minutiae as the features to align fingerprints. This is obviously problematic especially when fingerprint templates are also not complete.
Considering the case of using small sized fingerprint sensors, it is expensive to collect full fingerprint templates or to construct full fingerprint templates from partial fingerprints in enrollment. In forensic cases, it is possible that no full fingerprint templates are available. As a result, these methods will not be applicable. Therefore, to align fragmentary fingerprints, new features and new methods have to be exploited and devised.
Pores, as kind of fine fingerprint ridge features, have been recently explored in automatic fingerprint recognition with the aid of high resolution (≧1000 dpi) imaging techniques. They either statistically or experimentally prove the distinctiveness of pores and the effectiveness of pores in identifying persons. Among these studies, (Kryszczuk et al.) investigated the effect of pores in matching fragmentary fingerprints and they concluded that pores become more useful as the fragment size as well as the number of minutiae decreases. In their study, they did not discuss more about the alignment problem, but simply used a correlation based method. Stosz and Alyea, “Automated System for Fingerprint Authentication Using Pores and Ridge Structure”, proposed the first high resolution automatic fingerprint recognition system (AFRS) which uses both minutiae and pores. In their system, however, some regions have to be manually located to align fingerprints. More recently, (Jain et al.) proposed to use the features from level 1 to level 3 (i.e. orientation fields, minutiae, pores and ridge contours) in high resolution fingerprints for identification. The alignment of fingerprints in their method is based on minutiae. Note that both of the above two methods work with a large fingerprint region. These studies demonstrate that pores are very distinctive features on fingerprints and even the pores on a very small fingerprint area can distinguish persons. Some patents have also been issued which employ pores in the fingerprint recognition. The method disclosed in U.S. Pat. No. 6,411,728 (Lee et al.) extracts pores from normal low resolution (e.g. 500 dpi) fingerprint images. However, this is arguable because in recent FBI standard regarding Level 3 features the minimum resolution for reliable pore extraction is 1000 dpi. In the U.S. Published Application No. 20070230754 (Jain et al.), instead, 1000 dpi fingerprint images are used and features from level 1 to level 3 are used together in either a parallel or a hierarchical way to recognize fingerprints.
Extraction of pores is surely an important step in the fingerprint recognition systems that use pores. Sweat pores reside on finger ridges, being either open or closed. An open pore is perspiring and appears on fingerprint images as a bright blob connected with the bright valley, whereas a closed pore appears as an isolated bright blob on the dark ridge (refer to FIG. 1). To the best of the inventors' knowledge, the first pore extraction method, proposed by Stosz and Alyea, binarizes and skeletonizes the fingerprint image. A pore is detected once some criteria are met while tracking the skeleton. This skeletonization-based method was later used in Lee et al. However, skeletonization is computationally expensive and very sensitive to noise. It can work well only on very high resolution fingerprint images, e.g. the fingerprint images used in Kryszczuk et al. were at least 2000 dpi. Recently, Ray et al. “A Novel Approach to Fingerprint Pore Extraction” proposed an approach to extracting pores from 500 dpi fingerprint images using a pore model (refer to FIG. 2(a)), which is a slightly modified 2-dimensional Gaussian function:
                              M          ⁡                      (                          i              ,              j                        )                          =                  1          -                      ⅇ                          -                                                                    i                    2                                    +                                      j                    2                                                                                                          (        1        )            
Pores are found by locating local areas that can match to the pore model with minimum squared errors. This method uses a filter of universal scale to detect pores. However, it is hard, if not impossible, to find a universal scale suitable to all pores. Moreover, the pore model (1) is isotropic. Very recently, Jain et al. proposed to use the following Mexican hat wavelet transform to extract pores based on their observation that pore regions typically have high negative frequency response as intensity values change abruptly from bright to dark at the pores:
                              w          ⁡                      (                          s              ,              a              ,              b                        )                          =                              1                          s                                ⁢                      ∫                                          ∫                                  R                  2                                            ⁢                                                f                  ⁡                                      (                                          x                      ,                      y                                        )                                                  ⁢                                  ϕ                  ⁡                                      (                                                                                            x                          -                          a                                                s                                            ,                                                                        y                          -                          b                                                s                                                              )                                                  ⁢                                                                  ⁢                                  ⅆ                  x                                ⁢                                  ⅆ                  y                                                                                        (        2        )            The scale s in this pore model is experimentally set with a specific dataset. FIG. 2(b) (Jain's pore model) shows the shape of the Mexican hat wavelet. Obviously, it is isotropic. This pore model is also limited by that the pore extractor cannot adapt itself to different fingerprints or different regions on a fingerprint. Almost all existing pore extraction methods use some a priori knowledge to post-process the pore extraction results. For example, both Jain et al. and Ray et al suggest using the ridges as a mask to filter out spurious pores. In jain at al., the sizes of true pores are constrained to be within a range.
In practical fingerprint recognition systems, either fingerprints of different fingers or fingerprints of the same finger could have ridges/valleys and pores of very different widths and sizes. Such problems become even worse when using high resolution fingerprint scanners. From the example fingerprint images shown in FIG. 1, one can easily see the variations in ridge/valley widths and pore sizes over different fingerprint images as well as across the same fingerprint. Besides, by manually marking and cropping hundreds of pores in several fingerprint images, including both open and closed pores, three types of representative pore structures can be summarized as shown in FIG. 3(a-c). Among them, the last two types correspond to open pores and they are not isotropic. Therefore, the pore models used in Jain et al. and Ray et al are not accurate. To conclude, more accurate and adaptive pore models are needed for precise pore extraction from fingerprint images.